The large amount of data in the Internet has made information-collecting intimidating. When entering a Master's or Ph.D. programme, final-year undergraduate students may spend weeks or even months looking for the right advisor. Similar situations occur when postgraduate students are looking for academic positions, like research assistant, postdoc fellow, or research associate. Candidates have specific needs or requirements when they start their “hunt”. For example, they have particular research interests, and a preferred location of a university, training style, research subject, and so forth. Such information is freely available on websites and social media, but collecting and processing all this information takes a lot of time and effort.
We can make this process much easier and more efficient by leveraging data science. We aim to develop a platform where information on academic position vacancies and the biographies of professors are continuously collected. Leveraging the collected data, we can use artificial intelligence (AI) to find the best match for an academic supervisor for new candidates. Our novel approach will reduce processing time from weeks or months to just a few hours. It will ultimately lead to time-saving and process optimization to help candidates find the right academic position or advisor.
Team members
Dr Michele De Filippo (The Hong Kong University of Science and Technology)
* Person-in-charge
(Info based on the team's application form)
- CityU HK Tech 300 Seed Fund (2022)